• 31% of your work day is made up of purely ad-hoc, never happens the same way twice tasks
• 30% of your work revolves around consistent, defined goals but various ways to achieve those goals
• 20% of your work involves documented and managed tasks that are not automated
• 17% of your work is automated but there are numerous exceptions to the automated processes
• 9% of your work is fully automated and there are no ways to change the process (Fischer, 2011, p. 84)

Except for a very few exceptions, every government worker is a knowledge worker because they deal with constantly varying situations that we package into cases. In general, we may deal with specific subject areas and perform repeatable functions but the actual execution of this work will differ greatly from case to case. For example, when I was a paralegal/investigator for a public defender’s office, I helped on numerous assault cases. We had a specific process for interviewing the client, preparing the pleadings, assembling the evidence, and presenting the case. But the facts of the case were always different.

One case was about an assault by a drunken student on an equally drunk off-duty policeman. Another case was a domestic violence issue while a third involved a store employee who tackled a complaining customer. For each case, the kind of pleadings filed, how I conducted the investigation, and so on would differ based on the specific events in that case. You really didn’t know what was going to happen from day to day so it was difficult to determine routines beforehand.

This is why I don’t believe that the best way to improve government work is to start imposing Six Sigma and Lean processes onto government employees. Six Sigma and Lean are great methods if you are talking about repeatable processes that have clear paths and outcomes. But as the above statistics demonstrate, less than 10% of a knowledge worker’s day will benefit from traditional business process management techniques.

On the other hand, traditional case management as practiced by many government workers has many problems. Most government offices have overwhelming case loads, there are conflicting rules and procedures imposed by the top management, and the current support systems cannot easily handle the many exceptions that occur frequently (Swenson, 2010, pp. 10-24). What is needed is a way that allows for the great variation in knowledge work but makes that knowledge work more efficient and effective. I believe that the newly emerging management concept of Adaptive Case Management (ACM) is the answer along with its closely-allied discipline of Social Business Process Management (SBPM) (Fischer, 2011).

ACM is still evolving but there are several core elements. First, instead of being based on the principles of scientific management/Taylorism, it revolves around modern knowledge work. This means that ACM is designed to deal with change and ad-hoc processes as a case is being processed. Second, processes are not formalized and designed up front but are developed as the knowledge worker continues to see the same issue in a number of cases. Third, rules and regulations operate more like guardrails that constrain the actions taken in a case. The fourth element is that the knowledge workers rely heavily on a community-built template library and body of knowledge that is built collaboratively in the organization.

This is why ACM relies so heavily on social networking in the form of SBPM. In traditional business process modeling, discovering what processes exist and modeling these processes were done first and then the knowledge workers were expected to follow the newly-established processes until the weight of exceptions demonstrated that the new processes needed to be modified. Under SBPM, process discovery and modeling occurs as knowledge workers work on cases and share their experiences with each other. Thus, there is a great deal of variation at first in handling cases but as the knowledge workers gain more experience, they collaboratively develop best practices that can easily be modified when exceptions occur.

I have just given the briefest overview of these two new management concepts but I am greatly excited by the potential to reform government work for the better. There are numerous case studies in Taming the Unpredictable including how one local government agency used ACM for better customer service in its case management processes. Much of ACM and SBPM makes intuitive sense and should be especially attractive to those who argue we need more knowledge sharing and collaboration in our offices.

(Disclaimer: All opinions in this posting are my personal thoughts and do not reflect upon my employers or any organizations I belong to.)

References:
Fischer, L. (editor) (2011). Taming the unpredictable: Real-world adaptive case management: Case studies and practical guidance. Lighthouse Point, FL: Future Strategies, Inc.

Fischer, L. (editor) (2011). Social BPM: Work, planning, and collaboration under the impact of social technology. Lighthouse Point, FL: Future Strategies, Inc.

Swenson, K. D. (editor) (2010). Mastering the unpredictable: How adaptive case management will revolutionize the way that knowledge workers get things done. Tampa, FL: Meghan-Kiffer Press.

Additional Resources:
Law and Order: How Adaptive Case Management Serves the Public Good – http://community.global360.com/bpm_practitioner/b/weblog/archive/2011/08/11/law-and-order-how-adaptive-case-management-serves-the-public-good.aspx

Simplify Your Work Life: Adaptive Case Management – http://i-sight.com/tech/adaptive-case-management/

The Future of Adaptive Case Management – http://www.industryweek.com/articles/the_future_of_adaptive_case_management_22981.aspx

What is Adaptive Case Management? – http://www.cmswire.com/cms/enterprise-cms/what-is-adaptive-case-management-008277.php

Adaptive case management: New tools for doing more of what we do best – http://www.kmworld.com/Articles/Editorial/Feature/Adaptive-case-management-New-tools-for-doing-more-of-what-we-do-best-74486.aspx

What Could Cause Adaptive Case Management to Fail in 2011 – http://blog.actionbase.com/what-could-cause-adaptive-case-management-to-fail-in-2011

So, what do we mean when say a problem is complex? According to Dr. Melanie Mitchell, there are nine definitions for complex as used by complexity theorists. These definitions range from “complex as a matter of size” to “complex as a degree of hierarchy” to “complex as a measure of algorithmic information content” (pp. 96-111). I tend to think of complexity in terms of systems theory in which you have a number of discrete components with numerous feedback loops and many variables that are hidden within the system processes.

A good example of a complex system is the American economy. There are many discrete components in the forms of companies, consumers, banks, regulatory agencies, etc. all passing information to each other and reacting to that information. Attempts to model the American economy range from the simple macroeconomic diagrams in textbooks to detailed microeconomic equations that requires years of mathematical study to even understand. Yet these models, no matter how detailed, cannot fully describe and fully predict how the American economy operates.

If you accept my definition of complexity then you can see how the next concept describes why complex problems are hard to solve. We have difficulty in solving complex problems because our observation of the problem is hindered, we cannot fully understand the problem, our decision-making processes are flawed, or we cannot act appropriately in confronting the problem. If any of the difficulties I mentioned sound familiar it is because I am describing the four components of the “OODA Loop.”

The Observe-Orient-Decide-Act Loop (OODA) was created by Colonel John Boyd who was a fighter pilot and scholar in military strategy. This concept has been adopted both by the U.S. military and championed by such business experts as Tom Peters. As the diagram below demonstrates, a person, team, or an organization observes a situation along with other inputs. Based on the observations and several internal factors, the subject attempts to orient themselves or understand the unfolding situation. Based on that understanding, the subject then makes a decision and acts upon that decision. Throughout the OODA Loop, there are several feedback channels that make the entire process nonlinear.

Colonel Boyd explained that the use of the OODA Loop was to travel through the Loop faster than your opponent. You present confusing and ambiguous information to your opponent so that they have difficulty orienting themselves and thus are slower to decide and act. Essentially, you want to go through your own OODA Loop faster than your opponent does so that they start falling behind and then are paralyzed by their inability to analyze the situation. Time is the key factor in OODA Loops.

The OODA Loop is why I think complex problems are so difficult to solve. Consider the five components of the OODA Loop as it applies to your personal abilities or the abilities of your team/organization:

• Observe: This is the beginning of the Loop and also feeds into another iteration of the Loop. If your observational abilities are hindered or you just cannot observe all parts of the unfolding situation then you are working with incomplete information. History is replete with examples where disasters occurred because of the lack of key information.
• Orient: This is where you/the team/the organization takes in the new information and pairs it with your previous knowledge, cultural traditions, and other internal factors that influence how you process and analyze information. So, even if you are able to observe the entire unfolding situation, your internal abilities to process and analyze this information can prevent you from fully understanding what is happening.
• Decide: This relates to your ability to generate hypothesis about the situation and possible responses. There is the common “paralysis by analysis” which hinders decision making because you are still trying to orient yourself to the situation. Or, even if the organization has a good understanding of the situation, decision processes may be so cumbersome that you cannot make a decision in time to act on the situation.
• Act: You may not have the resources to act promptly and/or appropriately. Your understanding of the situation may have led to a flawed decision that forces an invalid response to the situation. You do not have the proper feedback mechanism built in your action to determine how your act affected the unfolding situation.
• Feedback: As you go through the OODA Loop, you are constantly generating and receiving feedback from your current iteration and previous iterations. Without good feedback design, your own actions can contribute to the ambiguity of the situation. This is especially true of wicked problems where there is no consensus on the actual shape of the problem and your actions can drastically morph the problem into a completely new problem.

The good news here is that you can also use the OODA Loop to better your abilities to handle complex problems. Use the five components as a checklist for improving your (or your organization’s) processes in handling complex problems.

For example: how well do you observe? How good is your organization at collecting and disseminating information internally? Do your people have the necessary prior knowledge and analysis skills to properly orient themselves when new observations come in? How robust and quick is your team’s decision-making skills? What barriers can you remove so that you can act faster? What can you do to improve your feedback mechanisms?

Government is going to face more complex problems especially in a climate of reduced budgets and increasing responsibilities. All government employees at all levels need to sharpen their problem-solving skills so that we are more innovative and can better tackle the looming wicked problems that face the nation. Whether you accept my suggestion to use the OODA Loop or come up with your own problem solving method, the process of thinking about complex problems is a great way to sharpen your problem solving skills.

Reference:
Mitchell, M. (2009). Complexity: A guided tour. New York: Oxford University Press.

It was that advice that has guided me ever since. It is very easy in the euphoria surrounding the triumph of solving a difficult problem or pulling off the near-impossible project to not spend the time questioning just why you succeeded. To do so seems to be diminishing the success and even doubting that you actually did succeed. On the contrary, an objective review of how you succeeded will greatly help you in continuing to succeed.

When we succeed, we can become victims of three biases, according to Gino and Pisano (April 2011). There is the attribution bias in which we overestimate how our knowledge and actions contributed to the success and we downplay any external factors that could have just made us more fortunate. We also become overconfident in our abilities as we tackle the next challenge. The third bias (and which I believe is most important) is that we don’t ask why we succeeded because the success is proof enough.

To illustrate this last bias, Gino and Pisano (April 2011) recount a study in which students were given a set of math problems to complete. When the students submitted their answers, they were only told if they had the answer right or wrong. The students were given time to reflect before they were given a second set of math problems. The second set was designed so that a key concept in the first set of problems was needed to solve the second set. The students who successfully solved the first set of problems generally spent much less time reflecting before they started on the second set of problems. Thus, many of these students failed to find the answer for the second set of problems. Reflection, whether the student succeeded or not, is the key to continuing to be successful.

So, how do we best learn from success? We should celebrate success but also examine the causes of success. For every project, we should hold a systematic review. Gino and Pisano (April 2011) give the example of Pixar’s review process. Even though Pixar has had eleven hit animated films in a row, the company still goes through an exhaustive review process to determine what made the film successful and how to repeat that success.

Another point to keep in mind is to fully investigate the feedback. Was it immediate or at least can be connected to the actions taken? Is the feedback a true indicator of success or just a random event that looks like a successful outcome? Feedback is an important concept and I explore it in greater detail in this discussion posting.

Two final points. First, “[r]ecognize that replication is not learning” (Gino and Pisano, April 2011). Blindly following the same formula again and again can suddenly turn against us as the nature of the problem changes and what worked before doesn’t work now. And, second, we should always be experimenting. We can always improve how we do something. Plus, we can create variations on our actions that may not apply to the current situation but can apply to a challenge in the future.

Failure is a great teacher but so is success. Learning from our successes will keep us from becoming “one-hit wonders” and give us the string of successful “hits” to be “rock stars.”

Reference:
Pino, F., & Pisano, G.P. (April 2011). Why leaders don’t learn from success. Harvard Business Review. 68-74.

This is not a new method of innovation; in fact it is the oldest method of innovation around – evolution. Nature is constantly creating variations of species and then selecting the species that best survive current conditions. What Hartford does is apply that concept to organizations to see if a similar process works in determining what companies succeed and which go out of business. The organizations that best survive a constantly changing business environment are the ones that combine incremental improvement along with the occasional long-shot idea to propel them into a better part of the business landscape ahead of their competitors

So, what does this have to do with government agencies? Hartford flatly states that this innovation method will not work in government agencies because of several barriers. First, there is not enough time for political appointees to fully see these experiments through before a new administration comes in office. Second, the process depends on a large number of failures for innovation but failure carries a high stigma in government. Third, it is difficult to clearly demonstrate that a policy innovation actually had an effect due to the lack of robust feedback loops in government.

I believe that Hartford’s opinion about government innovation is way overstated. There have been numerous government projects that have been extremely innovative: the Hoover Dam, rural electrification, the Interstate Highway System, the Moon landings, the Space Shuttle, the Internet, and so on. Even so, when you examine how these agencies developed these projects you do see that these agencies tried many ideas and learned from these trials. NASA has an amazing knowledge management culture and DARPA’s successful record of innovation is built on the concept of trying many long-shot ideas at once.

What holds government back from being even more innovative is the stigma of failure. Many agency cultures are too cautious because of the constant external scrutiny and the internal cultural practices of not sticking your neck out and just waiting out the latest change effort. In many cases, this caution is well-warranted. Many people depend on government agencies and thus agencies cannot fail in their primary mission of delivering Social Security checks, defending the nation, or enforcing laws and regulations

But failure to innovate will also lead to mission failure for agencies. In the sixth chapter of his book, Hartford describes how the 2008 economic meltdown was inevitable given the tight coupling of economic institutions and the failure of the government to prevent financial organizations from becoming too entangled. He argues that in any complex system, accidents will normally occur and that often our failure-prevention efforts will only increase the probability of failure. What is needed are the twin strategies of placing buffers between parts of the system and setting up feedback loops to warn us of emerging failure events.

Government has to constantly innovate so that it can continually deliver on its mission. This means that the culture has to change so that the agencies accept the small failures that teach to avoid the large failures that cripple the agency and harm the people it serves. Whether we call it “experimentation”, “pilot tests”, or some other euphemism, the better government is at innovation the better it can serve its citizens.

Disclaimer: The opinions in this posting are solely mine and do not reflect the views and opinions of my employers or any organizations I belong to.

References:

Belfiore, M. (2009). The Department of Mad Scientists: How DARPA Is Remaking Our World, from the Internet to Artificial Limbs. Smithsonian.

Hartford, T. (2011). Adapt: Why Success Always Starts with Failure. Farrar, Straus and Giroux.

Culture is a natural byproduct of humans as social beings. We develop culture so that we can get along, survive, and achieve goals. It is only naturally that we develop cultures at work because a large part of our waking hours is spent at work or thinking about work. A single person cannot have a culture; it takes interactions between each other to create a culture. But what exactly is culture?

There are many academic definitions for culture but for our purposes I prefer this simple definition: the way we do things around here. “We” come together in a defined group (eg. IBM, HUD, Star Trek fans) and in a defined boundary such as a department, office, or online community (“around here”). We develop methods, practices, policies, etc. (“way”) that govern the actions (“do”) members of the culture take in response to “things” (issues, events, etc.) that we face as a culture. Essentially, organizational culture is how we collectively solve the problems we face everyday in our work and life.

Thus, the resistance to changing the organizational culture. Problem-solving is hard and takes a lot or resources and effort. Humans are incredible at problem-solving but they are also good at optimizing. We don’t like having to solve the same problems over and over so we create things like writing, forms, email, databases and the like to embody the solutions we have created so that the next time the same problem shows up we can just solve it without having to think about it. We only give up our solutions when a demonstrably better solution comes along. And it better be a really good solution if it has a chance of displacing the current solution.

Cultural change is not only possible but it is necessary. Groups change, new events confront the group, and new problems face our culture. I believe that the reason why many intentional cultural efforts fail is that they don’t recognize the paradoxes of cultural change. This is what I can the Zen of culture because we blend many paradoxical ideas to develop culture. Here are three paradoxes that makes cultural change difficult for those who do not first seek to understand the culture:

1. The culture is not the culture. There is no one culture but many cultures that people belong to. You may have an overall agency culture but you also belong to the subculture of your department, the subculture of Redskins fans, the subculture of people who eat out for lunch, and so on. Some of these subcultures are easily changed while others are ingrained in you. And how these subcultures interact cause the resistance to change. For example, IT folks are often most open to new technologies while the law department would rather stay with the software they have been using for the last 20 years because they have built many of their processes around how the software works.
2. We seek the novel and the safety of the familiar. A colleague has a great example of this. Imagine a playground in an open field without fences. The children will often huddle together in the middle of the playground and are reluctant to wander out in the field. Now, put a fence around the playground. Then the children will often hang around the fence and are more willing to venture out in the open field. It is the setting of boundaries that makes us adventurous. We have the safety of the fence that we can run to in between our adventures. Culture equals safety.
3. Culture remains the same by changing. Thanks to the Internet, many ancient religions are now being practiced today. Many Amish businesses use a personal computer in their business dealings with the outside world. There are numerous monasteries that sustain themselves by creating websites for clients. Cultural groups will often use the new technologies or practices as a way to sustain the culture and its core beliefs. This can be frustrating for a change agent when they see their innovation being used to defeat the intent of the change.

I want to invite you to join the conversation on cultural change and keep Open Government alive. There is well-justified concern that the 2011 budget cuts to the Open Data sites will stall the Open Government but I believe that the best way to keep Open Government going is to change the cultures in our agencies and governments so that the citizens demand further change. This concern has prompted several of us to start an online group with two purposes:

1. to collect the best thinking on how to effectively change cultures so that they are more open, transparent, and collaborative, and
2. to establish worldwide unconferences where government workers, academics, and citizens gather to discuss how to change the culture of their governments to be more open, transparent, and collaborative.

Please join us at Culture Change and Open Government.

(Disclaimer: The views expressed are my own and do not reflect the views of my employers or any groups where I am a member.)

I remember when I set up my Commodore 64 with a 300 baud modem to connect to my high school friend across the small town of Winchester, Kentucky so that we could type text messages to each other. I spent an entire weekend typing in machine code from RUN Magazine to create a bulletin board system (BBS). My parents didn’t understand why I went to all this trouble when I could have just called Steve, mailed him a letter, or just see him at school the next day. Today, billions of texts and tweets are sent daily all thanks to the network effect.

The Internet has fundamentally changed our world because it has helped us connect on a level never seen before in human history. I don’t believe we can go back to a time before we had the Internet because so much of our current economic and societal systems depend on this connectivity. And, according to William Davidow, we are realizing new dangers as we go from highly-connected to overconnected.

Davidow argues that as complex dynamic systems (such as economies) become more and more connected they shift from stability to instability. There is a cultural lag as organizations and societies cultural practices lag behind technological advances. Institutions begin to falter because they are not flexible enough to keep up with the rapid changes and increasing demands of more and more connections. We enter a vulnerability sequence as positive feedback from the connections lead to more specialization and network lock-ins.

Davidow gives numerous examples of the dangers of overconnection such as Three-Mile Island, the decline of the American steel industry, and the 2008 mortgage meltdown. His best example is a two-chapter examination of how Iceland’s attempt to be an Internet banking superpower led to the collapse of the Iceland economy and government. Here we can see how positive feedback driven by the Internet led to riskier investments by Iceland banks and citizens that made them very vulnerable to an external event – the collapse of Lehman Brothers. Thanks to a chain of connections from New York to London to Paris and so on, the ripple effects from Lehman Brothers collapse were magnified so that the ripple became a tsunami that led to a massive devaluation of Iceland’s currency.

That is the secondary danger of overconnection – the magnification of the effects of small events into greater dangers. You may have heard of the black swan theory in which Nassim Taleb describes events that are so highly improbable that they are hard to foresee but can have significant impact when they occur. Thanks to overconnection we are subject to more black swan events that have their effects magnified by the positive feedback of overconnections.

So what does this mean for government agencies? As agencies rush to increase social networking inside and outside of their organizations they are in danger of becoming overconnected. Can the agency’s culture deal with the increasing demands of the connections? Is the agency flexible enough to deal with the unexpected events that will come being more open to the world? Will the management even realize when a black swan event has occurred?

To combat the effects of overconnection Davidow describes three things organizations must do:

1. Provide buffers to mitigate the increasing positive feedback.
2. Develop more robust systems that can better handle system accidents.
3. Restructure organizations to be more effective and adaptable.

As we embrace Open Government we must realize that increasing transparency, openness, and collaboration has great benefits but can also lead to major unintended consequences. We need to strike that delicate balance between highly-connected and overconnected by moving at a pace where we transform agencies into more effective and adaptable organizations without going into a vulnerability sequence.

Reference:
Davidow, W.H. (2011). Overconnected: The promise and threat of the Internet. Harrison, NY: Delphinium Books.

The failure of many early collaboration systems to catch on was perplexing because software packages for individuals and organizations were doing well. What was it about developing software for groups that made it so different from developing software for individuals and organizations?

In 1994, Dr. Grudin (a computer scientist from the University of California) published an article that answered that question with the simple observation that groups were just different from individuals and organizations. How they are different is explained in his eight challenges for developers:

1. Who Does the Work and Who Gets the Benefits. Ideally the labor in operating and maintaining the groupware application must be roughly equal among the group members. In reality this is rarely the case. Consider a project management application where the team members are required to update it regularly with progress reports, performance data, and other data. A good deal of the team member’s team is compiling information and feeding the system while the project manager just has to spend a minimal amount of time reading reports the system generates. The team member sees only a burden from the software and soon starts to avoid doing this extra work which leads to poor reports causing the Project Manager to quit relying on the system for information. Soon, no one is using software.
2. Critical Mass of Users. The collaboration software field is filled with a number of different platforms for collaboration. Many offer similar features and each has its enthusiastic community of supporters. In large government agencies you can see several collaboration systems in various pockets of the organization that don’t communicate outside of their pocket. Ironically the systems that exist to promote collaboration often end up promoting organizational silos as the various groups argue that their system is the best solution.
3. Social, Political, and Motivational Factors. Dr. Grudin gives a great example of this challenge when he describes the failure of meeting management software. It assigned meeting rooms based on priority but quickly became useless because no one wanted to admit that their meeting was anything but “high priority.” As Dr. Grudin explains, collaboration software can only model a rational workplace but actual workplaces are much more complex due to organizational culture.
4. Exception Handling. We rarely work the exact way that is described in our work processes. Collaboration software built only based on the documented office procedures is seen as too rigid and not able to handle the flexibility required frequently at work. Just think of how often you don’t have a typical day at work and have to improvise a work solution. Now, imagine trying to program that into software.
5. Decreasing the Communication and Coordination Load. Organizations are designed to reduce the amount of communication and coordination needed to do the job. How many times have you said that you could get more done if you were not interrupted so often? Of these interruptions, how many were due to email, phone calls, a colleague stopping by to talk, etc.? Sometimes you can over-collaborate and this often is the result of poorly-designed groupware.
6. Hard to Evaluate Groupware. It is difficult to test groupware because the group dynamics are so hard to replicate. It can take several weeks of careful observation to fully understood how a group works and software designers just don’t have the time or expertise to fully evaluate how their software will aid in collaboration. Often the groupware vendor blames this on poor user training and will continue the same type of software with better tutorials and help aids but never realizing that the fundamental problem is that people just don’t like collaborating the way the system is forcing them to collaborate.
7. Intuitive Decision Making. Because of the nature of our work we often have to make decisions based on little evidence and thus we rely heavily on our intuition. Groupware applications rarely support intuitive decision making but rather force users to input great amounts of data so that a fully-reasoned decision can be made. Often we do not have all of the data and a decision must be made quickly so we abandon the groupware application to use a simple spreadsheet or other individual application to support our intuition.
8. Managing Acceptance of the Groupware. Too often I have seen a collaboration solution launched where the users are expected to adapt themselves to how the software works rather than the software adapting to the way the group works. There is a particular system at my work which is universally despised because it practically handcuffs a group of users to a cumbersome and protracted painful process. I’ve only used the system once but that was enough for me to avoid ever having even to click on the program icon.

Despite these principles being over sixteen-years old I still see the same mistakes being repeated in today’s Web 2.0 collaboration tools. I also see where companies have put these principles into practice and have made great collaboration software that has endured and grown in popularity. I fully suspect that Google engineers must have memorized these principles when they developed their Google Docs system. You can also see these principles at work in the various products from 37Signals and Zoho.

I leave a final exercise for the reader: how many of these principles does SharePoint violate (if any)? Or does SharePoint violate new principles of collaboration software?

Reference:

Grudin, J. (1994). Groupware and Social Dynamics: Eight Challenges for Developers. Retrieved at http://research.microsoft.com/en-us/um/people/jgrudin/past/papers/cacm94/cacm94.html.

Now on to the topic. There is a lot of cynicism both in and outside of government concerning Gov 2.0 and Open Gov. I believe most of this comes past government reform efforts which had, at best, mixed results. In my first round as a Federal employee I was involved with Gore’s Reinventing Government effort. Reagan had a blue-ribbon commission on reform as did Nixon. Government reform has been a continuing effort since Wilson and Taylorism. I believe they even found evidence of government reform efforts in Ancient Egypt (I wonder how you tell a god-emperor he or she needs to go “lean”).

So, with this long history of government reform which has some brought some innovation but disappointment what makes Gov 2.0 and Open Gov different? And is this difference enough to make a real impact? To me there are three major reasons why Gov 2.0 and Open Gov will succeed and succeed big:

1) Government is re-engaging their citizens. Public agencies can no longer operate as vending machines where citizens put in tax dollars and out pops government services. Agencies are becoming transparent and accountable for how they spend tax money and are encouraging citizens to become part of providing government services. Think of Dr. Noveck’s Peer-to-Patent program and the recent SeeClickFix programs.

2) Democracy is changing. I have written before about the emergence of monitory democracy and the recent events in Egypt, Libya, Yemen, and other Middle Eastern countries just demonstrates the desire for democracy and how media-rich nations empower citizens to grow democracy. Even in well-established democracies citizens are no longer content to vote for a representative and then trust that the government will operate in their interest. People want to speak directly to agencies that affect their lives and are demanding the right to monitor even the inner workings of the Federal, state, and local governments.

3) Balance of Information Power has shifted. Before the Open Data movement, government essentially held all the cards when it came to information about what government did and how it operated. Yes, there were investigative journalism articles, legislative hearings, and the occasional FOIA request but obtaining government information that wasn’t selectively released by agencies was difficult to obtain for the average citizen. Now, thanks to the Internet and Social Media technologies it is easier to gain access and to aggregate data sources to give a more complete picture of what government is doing. The Balance of Information Power is shifting in favor for the citizen and will continue to shift that way as government engages citizens and as monitory democracy evolves.

In Gov 2.0 and Open Gov the citizen is no longer a customer who passively receives government services. Past reform movements were built upon the vending machine model and that is why they didn’t deliver as promised. Gov 2.0 and Open Gov are built upon engagement and collaboration and that is why they will prove to be more successful.

What do you think? Are these three reasons valid? Are there better reasons for why Gov 2.0 and Open Gov will succeed? Or is Gov 2.0 and Open Gov just like previous government reform efforts?

Collaboration is important but it is not sufficient. Nicholas Charney noted this in a great posting where he questioned the value of collaboration as it was currently practiced in organizations. I commented that a tangible product from the collaboration would make the process better but I am becoming more convinced that even that is not enough. What is needed is something that would continue the benefits of collaboration between the collaboration sessions. A way of engaging the person’s thoughts and focusing those thoughts on the collaboration work even when the person is working alone. A process that I call collaborgagement. Not just a combination of collaboration and engagement but a process that is synergistic.

The foundation of collaborgagement is the mental model. The mental model has been variously defined by different fields but the consensus seems to be that mental models are “deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action” (Wind and Crook, 2005). Individuals have mental models but so do teams and departments. The purpose of the mental model is to make sense of various aspects of our lives including our work. Mental models take a great deal of effort to build but the benefit is, that once built, they reduce our thinking load.

For example, researchers have found that expert chess players actually think less than novice chess players because the expert chess player can focus on several pieces at once and perceive patterns of board arrangements. The novice chess player has to focus on separate pieces and build the pattern from the individual pieces. The expert chess player has a library of mental models they can consult that makes them better players because they can “look up” the answer to a chess problem while a novice is still calculating the problem.

The same process can be seen in everyday life. Think of how you learned to drive. Remember all the steps you had to master to start the car, put it in drive, and begin your journey. Repetition and observation helped you build a mental model so that driving almost becomes an automatic process requiring very little conscious thinking.

The challenge is that we rely on our mental models so much that we strenuously resist changing or discarding our existing models. This goes for team mental models as well as individual mental models. But our changing world requires that we change our mental models or they quickly lose their benefit and can even harm us in the new realities we face. We need a process of engaging peoples’ attention at the level of their mental models and then collaborate together to help explore current mental models and modify or even replace these mental models on an individual and team level. This is the purpose of collaborgagement.

There are probably several methods for examining current mental models and altering them but I like the process Wind and Crook (2005) outline in their book The Power of Impossible Thinking:

1. Understand the power and limits of mental models.
2. Test the relevance of your mental models against the changing environment, generate new models and develop an integrated portfolio of models.
3. Overcome inhibitors to change by reshaping infrastructure and the thinking of others.
4. Transform your world by acting quickly upon the new models, continuously experimenting and applying a process for assessing and strengthening your models. (p. xxiv)

With Wind and Crook’s (2005) process in mind this is how collaborgagment would work:

1. Before a team meeting the individual members examine their existing mental models that relate to the topic of the meeting. The team member may want to blog, mind map, or similar tool to help him or her to surface the mental models and produce it in a tangible form.
2. During the team meeting the individual members display their mental models. Then the team works together to surface the team mental models in a tangible form.
3. The team then examines the new reality of the topic and lists the characteristics. The goal of this phase is to come to a consensus about the new reality.
4. After a consensus has been reached, the team compares the current team mental model to the new reality. Does the team mental model need revising or is a completely new team mental model needed? The team works to determine the revisions or constructs the new mental model.
5. After the team meeting the individual members go on their own to reflect on the consensus about the new reality and how their current mental models compare to the new reality. The member then revises their existing mental models or constructs new mental models that reflect both the new reality and the team mental model.

What is important about this process is that it engages people on a deeper level than what usually happens in change efforts. I have been to plenty of meetings where great ideas and energy has been generated but it quickly dissipates once the meeting is over. For deep and sustainable change to happen you have to engage people at a fundamental level and produce collaboration that carries on ever after the meeting is over. I believe that starting at the mental model level is the best way to produce lasting transformative change.

Reference:
Wind, Y., & Crook, C. (2005). The power of impossible thinking: Transform the business of your life and the life of your business. Upper Saddle River, NJ: Wharton School Publishing.

Previous Posts on Collaboration and Engagement:
Without Engagement Gov 2.0 Will Fail
The Goal of Collaboration: Navigating the Network of Idea Spaces

What I took away from the conference was some ideas from Alfresco’s CTO, John Newton. He argues that enterprise IT has essentially been on hold since 2000 while consumer IT (Facebook, Twitter, etc.) has been on fire all throughout the first decade of the 21st century. Employees are demanding enterprise versions of what they use in their daily lives to connect and collaborate with their families. Not an original thought but a good summary of what is about to hit enterprise IT.

What was original and started me thinking was his later point that enterprise IT needs to build systems of engagement. That is, applications that focuses on the middle of the enterprise where the knowledge workers are. He states that we don’t need anymore applications for the frontline workers nor the top management because their needs are already met. I agree with the point about the top management but I am still not convinced about leaving out the frontline workers.

I do fully agree that the people in the middle of the enterprise do need better tools from enterprise IT. Tools that incorporate collaboration, knowledge sharing, and a whole host of activities under a new umbrella term that I just coined – collabogagement. A quick Google search shows that no one has used this term so I claim to be the first. In a future posting I will try to define it.